QR Image Generation

The QR code recognition often faces the challenges of uneven background fluctuations, inadequate illuminations, and distortions due to the improper image acquisition method. This makes the identification of QR codes difficult, and therefore, to deal with this problem, artificial intelligence-based systems came into existence. To improve the recognition rate of QR image codes, this article adopts an improved adaptive median filter algorithm and a QR code distortion correction method based on backpropagation (BP) neural networks.

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QR codes can be categorized as a 2D matrix code, which is a typical square-shaped code that can be determined by its dimensions and variations. As shown in Figure 1, the QR code can be structured into different modules like position markers, timing patterns, version number, format identifier, alignment marker, and data indicator [4,5].

• Position markers: This is the position detection indicator of a QR code, which is represented by a small square combined of a lighter and a darker square. It determines the position and orientation of the QR code.
• Timing pattern: They are the interconnected patterns formed by the alteration sequence of dark and light elements. They are responsible for determining the size, the number of rows and columns, and identification of distortion present in the QR code.
• Version number: This indicator in the QR code helps to identify the version number of the code.
• Format identifier: This indicator contains the information regarding the mask pattern number and the error correction level, which are needed to decode the QR code for identifying the type of content like URL, text, image, etc.
• Alignment marker: The alignment markers determine the possibility of distortion in the QR code by identifying the point of alignment in the code.
• Data indicator: The data encoded inside the QR code is decoded by the usage of this indicator. If the QR code is damaged, still it can be restored and read by using the error correction method